Measuring the increasing connectedness of Chinese assets with global assets: using a variance decompositions method

H, Yu; Sun, Wencong; Ye, Xiangting; Fang, Libing

doi:10.1111/acfi.12458

Cited by 4 publications

(1 citation statement)

References 45 publications

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“…Past studies have tested the wake‐up call hypothesis by assessing cross‐country equity volatility and crises transmission (Bekaert et al, 2014; Carvalho et al, 2015; Titman & Wei, 1999) and cross‐currency volatility transmissions (Gu & McNelis, 2013), and by comparing Islamic and non‐Islamic stocks (Akhtar & Jahromi, 2017). Yu et al (2019) investigate volatility spillover among stocks, bonds, foreign exchange and commodities in the US, EU, UK, Japan and China, while Kim and Yang (2008) test the impact of price limits on intra‐day volatility and information asymmetry in Taiwan. Studies focusing on REITs include volatility or return transmissions between REITs and stock markets (Case et al, 2012; Hoesli & Reka, 2013; Yang et al, 2012), between REITs and bond markets (Chong et al, 2009) and among EREITs, MREITs, equity and bond markets (Anderson et al, 2021; Chiang et al, 2017) among others.…”

Section: Review Of Relevant Literaturementioning

confidence: 99%

Transitory and permanent shock transmissions between real estate investment trusts and other assets: Evidence from time‐frequency decomposition and machine learning

Ngene

Wang

2023

Accounting & Finance

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We evaluate asset returns and volatility connectedness using the time‐frequency connectedness model and machine learning approaches. Using 48 years of monthly indices of equity real estate investment trusts (EREITs), mortgage real estate investment trusts (MREITs), stocks, commodities, and bonds, we find that shocks to EREIT, and MREIT returns have a transitory impact on other assets. However, asset volatility connectedness among assets occurs at lower frequencies as markets slowly process pricing information. Therefore, shocks to EREIT and MREIT decay gradually and spill over to the other three assets for long periods. We also find that the intensity of the time‐frequency connectedness of returns and volatility varies with business cycles and significant domestic and global non‐recession events. We attribute the dominance of real estate investment trusts (REITs) in destabilising the financial system through long‐term volatility transmission to the heavy linkage of REITs to highly illiquid underlying direct real estate markets and high REITs leverage, making them more sensitive to real estate fundamentals, monetary shocks and macroeconomic risks than stocks and bonds. The result of the algorithm‐based machine learning model broadly supports the dominance of EREITs in the transmission of returns and volatility and shows that commodities have more explanatory power in the transmission of volatility than in returns. The empirical findings have implications for strategic and tactical asset allocation and policy design for market stability.

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Section: Review Of Relevant Literaturementioning

confidence: 99%

Transitory and permanent shock transmissions between real estate investment trusts and other assets: Evidence from time‐frequency decomposition and machine learning

Ngene

Wang

2023

Accounting & Finance

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Time-varying tail risk connectedness among sustainability-related products and fossil energy investments

Lucey

Ren

2023

Energy Economics

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Low‐ frequency versus high‐frequency housing price spillovers in China

Yang,

Li,

2024

Accounting & Finance

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Applying a novel high‐dimensional frequency‐domain‐based spillover index approach, this paper explores low‐frequency versus high‐frequency housing price spillovers among 70 Chinese cities. Intensive interactions among these cities were dominated by high‐frequency spillovers around the end of 2015, while low‐frequency spillovers have dominated since 2016. This coincided with dramatic changes in government policies, particularly the abolition of the ‘one‐child’ policy. Furthermore, city‐level economic factors were the primarily significant determinants of high‐frequency housing price spillovers within several months, while education was a major determinant of low‐frequency housing price spillovers in China, which was consistent with the distinction between work‐based and education‐based migration in China.

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Measuring the increasing connectedness of Chinese assets with global assets: using a variance decompositions method

Cited by 4 publications

References 45 publications

Transitory and permanent shock transmissions between real estate investment trusts and other assets: Evidence from time‐frequency decomposition and machine learning

Transitory and permanent shock transmissions between real estate investment trusts and other assets: Evidence from time‐frequency decomposition and machine learning

Time-varying tail risk connectedness among sustainability-related products and fossil energy investments

Low‐ frequency versus high‐frequency housing price spillovers in China

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